We make use of a variety of high-performance computing technologies to scale our models and data infrastructure to huge size, with the result that we can typically handle huge amounts of data and very large and long-run process dynamics in the systems that we build.

We often have a keen interest in the geography of the systems that we model. Spatial data have many unique qualities that often require dedicated computing schemes to handle them, visualize them, and add value to them. Similarly, devices and machines that rely on geography for their functioning—whether robots, positioning systems, or sensors—rely on geographical abilities to do their work, Moreover, many algorithms, for example for search, classification, recognition, and differentiation, invoke geographical facilities to make them run and to help them run with efficiency or at scale. A burgeoning field of geocomputing has emerged in support of new, geographically-focused computing. Much of what we do involves building geocomputation solutions to problems that we, and others, encounter.

The image above showsa time-slice of a massively dynamic cellular automata model that my student, Hai Lan, built. The model can scale to cover many millions of CA, and to achieve this scale we rely heavily on Hadoop.